generating-dataflow-pipeline

generating-dataflow-pipeline is a skill for Claude Code, Codex from OpenDCAI/DataFlow-WebUI. It costs 55 tokens per session (6,097 once invoked), scanned A, original, Apache-2.0.

A generator for standard DataFlow pipelines, which are ordered data-processing steps, based on a goal and a small JSONL sample file. JSONL stores one JSON object per line.

In plain words
What is it for?
Use it to build or repair pipelines for document processing, text transformation, or combining multiple fields, with the input file path and expected outputs.
Why use it?
It examines the sample’s fields and types, selects suitable processing steps, checks that fields flow correctly between them, and produces runnable pipeline code.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/opendcai/dataflow-webui/generating-dataflow-pipeline
Any agent
npx skills add OpenDCAI/DataFlow-WebUI --skill generating-dataflow-pipeline
Clone the repo
git clone --depth 1 https://github.com/OpenDCAI/DataFlow-WebUI

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for generating-dataflow-pipeline

README.md
[![agentmods](https://agentmods.dev/badge/skills/opendcai/dataflow-webui/generating-dataflow-pipeline.svg)](https://agentmods.dev/skills/opendcai/dataflow-webui/generating-dataflow-pipeline)
Your own site
<a href="https://agentmods.dev/skills/opendcai/dataflow-webui/generating-dataflow-pipeline"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-webui/generating-dataflow-pipeline.svg" alt="Measured on agentmods" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,097 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00055 $0.06097
Opus 5 $0.00028 $0.03048
Sonnet 5 $0.00011 $0.01219
Haiku 4.5 $0.00006 $0.00610

Measured 4d ago against content hash 69a2cd2d9cd3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

generating-dataflow-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (templates/pipeline_template.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/canonical/generating-dataflow-pipeline/SKILL.md · 455 lines

How it starts

The opening of the file, as written. The whole thing — 455 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DataFlow Pipeline Code Generator

Goal

This skill is used when users provide:

  • Target: What the pipeline should achieve
  • Sample Data File: Path to a JSONL file containing 1-5 representative data samples

The skill must:

  1. Read and analyze the JSONL file at the provided path
  2. Infer data structure, field types, and content characteristics
  3. Determine task type based on file content (document processing, text transformation, multi-field composition)
  4. Select appropriate operators from preferred primitives
  5. Validate field dependencies
  6. Output intermediate operator decision summary
  7. Generate standard DataFlow pipeline code with first_entry_file_name set to the user-provided file path

User Input Format

Users provide:

Target: [Clear task description]
Sample file: [Path to JSONL file, e.g., ./data/input.jsonl]
Expected outputs: [Optional field list]

Important: The sample file is a JSONL file (one JSON object per line), not a JSON array.

Preferred Operator Strategy

Six Core Primitives (high-coverage operators for most data science tasks):

  1. PromptedGenerator - Single-field LLM generation
  2. FormatStrPromptedGenerator - Multi-field template generation
  3. Text2MultiHopQAGenerator - Multi-hop QA pair construction
  4. PromptedFilter - LLM-based quality filtering
  5. GeneralFilter - Rule-based filtering
  6. KBC trio (always used together in order): FileOrURLToMarkdownConverterFlashKBCChunkGeneratorKBCTextCleaner

These are preferred primitives, not fixed workflows. They can be used repeatedly and combined flexibly.

Operator Selection Priority Rule (MANDATORY)

When a specialized operator exists for the task, it MUST be used over generic operators. Do NOT use PromptedGenerator to replicate functionality that a dedicated operator already provides.

Decision table (check in order, use the first match):

Task / Scenario Required Operator Do NOT use
Generate QA pairs from text Text2MultiHopQAGenerator PromptedGenerator with QA prompt
Convert file path / URL to text KBC trio (FileOrURLToMarkdownConverterFlashKBCChunkGeneratorKBCTextCleaner) PromptedGenerator to summarize files
Score / evaluate using multiple fields FormatStrPromptedGenerator + GeneralFilter PromptedFilter (single input_key only)
Filter by deterministic rule on existing fields GeneralFilter PromptedFilter
Generate new content from a single field PromptedGenerator
Generate new content from multiple fields FormatStrPromptedGenerator Multiple PromptedGenerator steps

Read the full file on GitHub · 455 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 455 lines · 55 tokens per session scan A 69a2cd2d9cd3

Subscribe to this mod's changes

generating-dataflow-pipeline is a skill published in the GitHub repository OpenDCAI/DataFlow-WebUI (224 stars, last pushed 8d ago), licensed Apache-2.0. It adds 55 tokens to every session and 6,097 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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